### Install VARS Dependencies

Source: https://github.com/soccernet/sn-mvfoul/blob/main/README.md

This sequence of shell commands outlines the necessary steps to set up the environment for the VARS. It includes creating a Conda environment, activating it, installing PyTorch with CUDA support, the SoccerNet library, project-specific requirements, and the pyav library.

```Shell
conda create -n vars python=3.9

conda activate vars

Install Pytorch with CUDA : https://pytorch.org/get-started/locally/ 

pip install SoccerNet

pip install -r requirements.txt

pip install pyav

```

--------------------------------

### Install VARS Environment and Dependencies

Source: https://github.com/soccernet/sn-mvfoul/blob/main/VARS interface/ReadMe.md

This snippet details the steps to create a Conda environment for the VARS project and install necessary Python packages, including the 'av' library, using pip.

```bash
conda create -n vars python=3.9

conda activate vars

pip install -r requirements.txt
pip install av
```

--------------------------------

### Install VARS Dependencies

Source: https://github.com/soccernet/sn-mvfoul/blob/main/README.md

Installs all necessary dependencies for the VARS interface using Conda and pip. This includes creating a dedicated Conda environment, activating it, and installing requirements from a file, along with the 'av' library.

```Shell
conda create -n vars python=3.9

conda activate vars

pip install -r requirements.txt
pip install av
```

--------------------------------

### Run VARS Interface

Source: https://github.com/soccernet/sn-mvfoul/blob/main/README.md

Executes the main script for the VARS interface after dependencies are installed and model weights are downloaded. Users can then select video clips from the 'Dataset' folder for analysis.

```Python
python main.py
```

--------------------------------

### Download SoccerNet MVFoul Data

Source: https://github.com/soccernet/sn-mvfoul/blob/main/README.md

This Python code snippet demonstrates how to use the SoccerNet downloader to fetch the MVFoul dataset. It requires specifying a local directory and providing a password obtained after agreeing to an NDA. The 'downloadDataTask' method allows downloading specific splits like 'train', 'valid', 'test', and 'challenge'.

```Python
from SoccerNet.Downloader import SoccerNetDownloader as SNdl
mySNdl = SNdl(LocalDirectory="path/to/SoccerNet")
mySNdl.downloadDataTask(task="mvfouls", split=["train","valid","test","challenge"], password="enter password")
```

--------------------------------

### Run VARS Annotation Tool

Source: https://github.com/soccernet/sn-mvfoul/blob/main/VARS interface/ReadMe.md

This command executes the main Python script for the VARS annotation tool, which is used for processing camera shots and replays after the environment and dataset are set up.

```python
python main.py
```

--------------------------------

### Train VARS Model

Source: https://github.com/soccernet/sn-mvfoul/blob/main/README.md

This Python command initiates the training process for the VARS model. It requires the path to the dataset as a primary argument. Additional optional arguments allow for customization of the training process, such as specifying pooling types, frame ranges, FPS, pre-trained models, and model weight paths.

```Python
python main.py --path "path/to/dataset"
```

```Python
python main.py --pooling_type "attention" --start_frame 63 --end_frame 87 --fps 17 --path "path/to/dataset" --pre_model "mvit_v2_s" --path_to_model_weights "14_model.pth.tar"
```

--------------------------------

### MV-Foul Prediction JSON Format

Source: https://github.com/soccernet/sn-mvfoul/blob/main/Evaluate/README.md

This JSON structure represents the predicted labels for MV-Foul recognition. It includes the action class, offense status, and severity for each detected event, keyed by frame number.

```json
{
    "Actions": {
        "0": {
            "Action class": "High leg",
            "Offence": "Offence",
            "Severity": "3.0"
        },
        "1": {
            "Action class": "Standing tackling",
            "Offence": "Offence",
            "Severity": "1.0"
        },
        "2": {
            "Action class": "Challenge",
            "Offence": "No offence",
            "Severity": ""
        },
        "3": {
            "Action class": "Tackling",
            "Offence": "Offence",
            "Severity": "3.0"
        },
        "5": {
            "Action class": "Pushing",
            "Offence": "Offence",
            "Severity": "5.0"
        },
        ....
}

}
```

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